This project aims to develop and evaluate improved statistical methods for cancer prevention research. Biomarkers may be used in a number of phases of cancer prevention research, including studies of cancer etiology, risk assessment, and early detection. Biomarker methods addressing the needs in these areas will be identified and evaluated and evaluated, with emphasis on medical diagnostic tests (cancer screening) comparing and combining multiple biomarkers; the identification of population heterogeneity by using biomarkers, in particular comparing non-nested models; and missing biomarkers data methods. Group Randomized Trials, in which groups rather than individuals are randomized into treatment conditions, are of central importance to community-based cancer prevention research, evidenced by the large number of GRTs conducted in the past decade. A Finite Mixture Model (FMM) with random effect will be proposed and its properties and usefulness will be evaluated for identifying subpopulations and for appropriately modeling of nuisance over-dispersion, both in GRTs and in biomarker studies. Collectively, the proposed research has the potential to increase the efficiency and reduce the cost of cancer prevention studies, and to enhance the scientific and public health knowledge gained.